As we established in Post 1, AI systems can now understand and generate human-like text, music, and time-series forecasts. Unlike the CNNs we explored in Post 11, RNNs are designed for sequential data like language, speech, and financial time series. The transformer architecture we mentioned in Post 2 revolutionized how machines model sequences.
🔁 Introduction: The Challenge of Sequence
Sequential data isn't just about static input — it's about order, context, and memory.
Sentences have grammar and temporal dependencies.
Time-series data like stock prices depend on previous trends.
Music and speech require memory of prior tones or words.
Let’s explore the architectures that tackle these challenges.
🔄 RNNs: Memory Through Recurrence
Recurrent Neural Networks (RNNs) introduce cycles in the network so that information from earlier steps can influence later predictions.
Structure:
Takes input at time
tand outputs both:A prediction
A hidden state passed to the next time step
Equations:
h_t = f(W * x_t + U * h_(t-1))y_t = g(V * h_t)
Textual Diagram:
Input x_t → [RNN Cell] → Output y_t
↑
h_(t-1) ← Memory
⏳ LSTMs and GRUs: Solving the Vanishing Gradient
RNNs struggle with long-range dependencies due to vanishing gradients.
✅ LSTM (Long Short-Term Memory)
Introduced gates: input, forget, and output gates.
Allows network to remember or forget past info.
Used in translation, handwriting recognition.
✅ GRU (Gated Recurrent Unit)
Simplified LSTM with two gates: update and reset.
Faster to train, slightly less expressive.
⚡ Transformers: The Game Changer
The transformer architecture we mentioned in Post 2 revolutionized sequential learning by eliminating recurrence altogether.
📌 Key Ideas:
Uses self-attention to compute relevance of each word to every other word.
Processes entire sequence in parallel (vs. sequential in RNNs).
Enables long-range dependency modeling efficiently.
Architecture Components:
Multi-head self-attention
Positional encoding (since no recurrence)
Feedforward layers
Layer normalization & residuals
Textual Diagram (Encoder block):
[Input] → [Embedding + Positional Encoding] →
→ [Multi-Head Attention] → [Add & Norm] →
→ [Feedforward] → [Add & Norm] → Output
🧠 Applications of Sequence Models
1. Natural Language Processing
Machine translation (Google Translate)
Question answering (ChatGPT, BERT)
Summarization and text generation
2. Time Series Forecasting
Stock prediction, demand forecasting
Models like Temporal Fusion Transformer
3. Speech Recognition and Generation
Assistants like Alexa and Siri use RNNs or Transformers for audio input.
4. Biological Sequences
Protein structure prediction (AlphaFold uses attention)
Genomic sequence analysis
🏗️ Modern Language Models
Transformers form the backbone of today’s AI giants:
BERT: Bidirectional encoding
GPT family: Generative transformers
T5, XLNet, RoBERTa: Optimized for various tasks
These models are pre-trained on massive corpora, then fine-tuned on task-specific data, enabling few-shot and zero-shot learning.
🆚 CNNs vs. RNNs vs. Transformers
| Feature | CNN | RNN | Transformer |
|---|---|---|---|
| Best for | Images | Sequences (short) | Sequences (long) |
| Memory | No | Yes (via hidden state) | Yes (via attention) |
| Training | Parallelizable | Sequential | Fully Parallel |
| Applications | Vision | Speech, text | NLP, audio, bioinformatics |
🔮 What’s Next?
This post laid the groundwork for understanding how AI handles sequence and structure — from classic RNNs to transformer-based giants.
In the next post, we’ll explore industry-specific applications of deep learning — from healthcare diagnostics to financial risk prediction, and how you can build real-world models using open-source tools.
